title: Metric learning for automatic sleep stage classification.
abstract: We introduce in this paper a metric learning approach for automatic sleep stage classification based on single-channel EEG data. We show that learning a global metric from training data instead of using the default Euclidean metric, the k-nearest neighbor classification rule outperforms state-of-the-art methods on Sleep-EDF dataset with various classification settings. The overall accuracy for Awake/Sleep and 4-class classification setting are 98.32% and 94.49% respectively. Furthermore, the superior accuracy is achieved by performing classification on a low-dimensional feature space derived from time and frequency domains and without the need for artifact removal as a preprocessing step.
authors.forename: Huy
authors.initials: H
authors.lastname: Phan
authors.forename: Quan
authors.initials: Q
authors.lastname: Do
authors.forename: The-Luan
authors.initials: TL
authors.lastname: Do
authors.forename: Duc-Lung
authors.initials: DL
authors.lastname: Vu
mesh_terms: D000328:Adult; D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D004569:Electroencephalography / Q000379:methods*; D001330:Electronic Data Processing; D005260:Female; D006801:Humans; D008297:Male; D008991:Monitoring, Physiologic / Q000379:methods*; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012890:Sleep / Q000502:physiology*; D012894:Sleep Stages / Q000502:physiology*; D055815:Young Adult